xorbits.pandas.DataFrame.rank#

DataFrame.rank(axis=0, method: str = 'average', numeric_only: bool_t | None | lib.NoDefault = _NoDefault.no_default, na_option: str = 'keep', ascending: bool_t = True, pct: bool_t = False) → NDFrameT#

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Index to direct ranking. For Series this parameter is unused and defaults to 0.

  • method ({'average', 'min', 'max', 'first', 'dense'}, default 'average') –

    How to rank the group of records that have the same value (i.e. ties):

    • average: average rank of the group

    • min: lowest rank in the group

    • max: highest rank in the group

    • first: ranks assigned in order they appear in the array

    • dense: like ‘min’, but rank always increases by 1 between groups.

  • numeric_only (bool, optional) – For DataFrame objects, rank only numeric columns if set to True.

  • na_option ({'keep', 'top', 'bottom'}, default 'keep') –

    How to rank NaN values:

    • keep: assign NaN rank to NaN values

    • top: assign lowest rank to NaN values

    • bottom: assign highest rank to NaN values

  • ascending (bool, default True) – Whether or not the elements should be ranked in ascending order.

  • pct (bool, default False) – Whether or not to display the returned rankings in percentile form.

Returns

Return a Series or DataFrame with data ranks as values.

Return type

same type as caller

See also

core.groupby.GroupBy.rank

Rank of values within each group.

Examples

>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',  
...                                    'spider', 'snake'],
...                         'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df  
    Animal  Number_legs
0      cat          4.0
1  penguin          2.0
2      dog          4.0
3   spider          8.0
4    snake          NaN

Ties are assigned the mean of the ranks (by default) for the group.

>>> s = pd.Series(range(5), index=list("abcde"))  
>>> s["d"] = s["b"]  
>>> s.rank()  
a    1.0
b    2.5
c    4.0
d    2.5
e    5.0
dtype: float64

The following example shows how the method behaves with the above parameters:

  • default_rank: this is the default behaviour obtained without using any parameter.

  • max_rank: setting method = 'max' the records that have the same values are ranked using the highest rank (e.g.: since ‘cat’ and ‘dog’ are both in the 2nd and 3rd position, rank 3 is assigned.)

  • NA_bottom: choosing na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking.

  • pct_rank: when setting pct = True, the ranking is expressed as percentile rank.

>>> df['default_rank'] = df['Number_legs'].rank()  
>>> df['max_rank'] = df['Number_legs'].rank(method='max')  
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')  
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)  
>>> df  
    Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
0      cat          4.0           2.5       3.0        2.5     0.625
1  penguin          2.0           1.0       1.0        1.0     0.250
2      dog          4.0           2.5       3.0        2.5     0.625
3   spider          8.0           4.0       4.0        4.0     1.000
4    snake          NaN           NaN       NaN        5.0       NaN

This docstring was copied from pandas.core.frame.DataFrame.